Predicting with Confidence: Extensions to the GRNN Oracle Enabling Quantification of Confidence in Predictions

The GRNN oracle is an optimal estimator that provides the maximum likelihood unbiased estimate by combining a series of intelligent processing results, where those estimates with the smallest variance are weighted most highly. It is known that if the individual predictors in the ensemble are too similar, the oracle cannot provide much improvement. We have newly observed that if the predictions are characterized by class inhomogeneities, then the oracle can be limited in its ability to compensate. For some training cases, all models might provide incorrect predictions; let us call these cases “trouble makers.” To address this problem, the oracle theory was mathematically extended to provide estimates of the sensitivity of its predictions. These sensitivities provide a basis for declaring that certain of its predictions should be treated as untrustworthy. It then has information to flag them. This paper addresses that theoretical development and applies these extensions, to toy problems, with the future objective of application to real problems of detecting dementia / Alzheimer's in speech patterns.